The ever-increasing need for wireless capacity has recently triggered interest in switching from orthogonal to non-orthogonal signal transmission. When non-orthogonal signal transmission is allowed, interference occurs between different information streams. Examples of promising non-orthogonal approaches include traditional and distributed multi-antenna (MIMO) systems with aggressive spatial multiplexing, ultra dense network architectures, “Faster-than-Nyquist” signalling, non-orthogonal multiple access schemes and network coding. However, to deliver in practice the corresponding gains of non-orthogonal signal transmission as predicted in theory, it is necessary to be able to optimally, in the maximum-likelihood (ML) sense, demultiplex the mutually interfering information streams.
One well-known technique for optimally demultiplexing mutually interfering signals is sphere decoding, in which the corresponding exhaustive search problem is transformed to an equivalent tree search. However, the complexity of the sphere decoder increases exponentially with the number of interfering streams. The high complexity, in combination with the fact that the speed of traditional processors has reached a plateau in recent years, prohibits ML performance being achieved for a large number of interfering streams. Consequently, there has been interest in developing methods of parallelizing the sphere decoder which would enable the processing load to be divided into sub-processes that run on different processing elements. Sphere decoder parallelization is a very promising approach to reduce processing latency and power consumption. However, parallelizing the sphere decoder is not trivial. The efficiency of sphere decoding is dependent on the ability to early prune large parts of the sphere decoding tree without compromising optimality, and simple parallelization approaches can actually result in less efficient tree pruning, an increased number of calculations, and even increased processing latency.
The invention is made in this context.